/proteomics-quantification

name: proteomics-quantification
description: Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation),
  iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input
  is already protein-level (use proteomics-ms-qc); label-based TMT / iTRAQ workflows (search upstream
  first).
trigger: protein quantification, LFQ, TMT, DIA, DIA-NN, Skyline
tags:
- proteomics
- quantification
- lfq
- ibaq
- spectral-counting

proteomics-quantification

When to use

lfq sums intensities; spectral_count counts PSM rows; ibaq divides by supplied theoretical counts or a sequence-derived tryptic count. Use existing search-engine tables; this skill does not search raw spectra.

Use from a step

from skills._sdk.notebook import load_skill, write_output
library = load_skill('proteomics-quantification')
data = library.demo_data(random_state=42)
result = library.quantify(data)
write_output(result, 'tables/protein_abundance.csv')

For real data, use `read_input` and pass any `read_table` helper as `reader=`. The executable `examples/example_step.py` also checks the result and writes a Figure.

API

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`quantify(peptides: pd.DataFrame, *, method: str='lfq') -> pd.DataFrame`

Return a new protein abundance table without changing the peptides.

:param peptides: PSM rows with protein and method-specific intensity or sequence columns. :param method: CLI default lfq sums intensity; spectral_count counts rows; ibaq divides by theoretical peptides. :returns: Protein abundances with diagnostics in attrs. :raises ValueError: Required columns or positive theoretical counts are missing.

`run_info(table: pd.DataFrame, *, keep: bool=True) -> dict`

Read the diagnostics attached to a returned table.

:param table: Table returned by quantify. :param keep: True preserves attrs; the CLI uses False before serialization. :returns: A separate diagnostic dictionary. :raises TypeError: The input is not a DataFrame.

`abundance_figure(table: pd.DataFrame)`

Plot the distribution of protein abundance.

:param table: Output of quantify, including abundance. :returns: A matplotlib Figure; no files are written. :raises KeyError: The abundance column is absent.

`demo_data(*, random_state: int=42) -> pd.DataFrame`

Generate synthetic peptide data in memory.

:param random_state: CLI seed 42; change to generate another simulation. :returns: Synthetic peptides with protein sequences for iBAQ. :raises ValueError: The seed is invalid.

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Methods and parameters

lfq sums intensities; spectral_count counts PSM rows; ibaq divides by supplied theoretical counts or a sequence-derived tryptic count. Functions return new DataFrames. `run_info(result)` reads diagnostic attrs; use `keep=False` before serialization when those attrs are not needed.

Gotchas

• quantify rejects iBAQ without sequence or n_theoretical_peptides. LFQ is intensity summation, not MaxLFQ normalization.
• `demo_data` uses seed 42, matching the CLI; every demo is synthetic.
• `run_info` lives in DataFrame attrs and is not preserved by CSV serialization.

Inputs and outputs

The CLI reads CSV tables and writes:

• tables/protein_abundance.csv
• report.md
• result.json
• `reproducibility/commands.sh` records the CLI invocation template.

Functions return data and Figures without writing files. Steps own their outputs. Demo mode also writes its synthetic input when the original CLI used a file.

CLI

python skills/proteomics/proteomics-quantification/proteomics_quantification.py --demo --output /tmp/proteomics_quantification

For real input replace `--demo` with `--input <table>`.

See also

• `references/methodology.md`
• `references/parameters.md`
• `references/output_contract.md`
• `proteomics-data-import` for protein-table normalization; `proteomics-de` for comparisons.

Dependencies

`numpy`, `pandas`, `matplotlib`